Note that annotators almost never disagree between (CY, PY) and (CN, PN).

to their work, we (a) present a corpus with few invalid locations (⇡ 6%), and (b) work with finer-grained temporal information (when somebody tweets, within 24 hours before and after he tweeted, and longer than 24 hours before and after he tweeted).


Similar observations are true of an average case of literary encoding, to which grammatical information gets added for the purpose of enhancing searches or for basic measurements -it can be added as a separate document, i.e.

apart from @lemma and @lemmaRef, up till now TEI encoders could only resort to using the generic attribute @ana for inline linguistic annotation, or to the quite complex system of feature structures for robust linguistic annotation, the latter requiring relatively complex processing even for the most basic types of linguistic features.


2 1 The changes were merged in a pull request which also references detailed discussion, see https://github.com/TEIC/TEI/pull/1671 and issue #1670.

13 with the dependency captured at the level of markup while the representation proposed here is able to handle mild deviations from the 1:1 correspondence between word forms and tokens, it is not sufficient for handling complex multi-word units or for syntactic description -these require more powerful descriptive mechanisms.


We hope to introduce the head directionality parameter for UD guidelines.

a bunsetsu dependency-based treebank does not include the syntactic relation information.


We use Rhetorical Structure Theory (Mann and Thompson, 1988) as the theoretical framework of the corpus.

the relational taxonomy used in our annotation is the one used in the PCC (Stede, 2016), which is based on the relation set proposed in the original RSt paper (Mann and thompson, 1988).


The first component analyzes candidate words within a sentence and duplicates the sentence if there are two or more words marked as candidate and not all of them are verbs.

the information contains either new position in the sentence, parent token and dependency relation, or a marker that the token should be deleted.


We iteratively adjusted the conversion rules, manually checking output samples, making the rules more strict and precise, and re-running them.

all input data are in the CoNLL-U format.


It matches the pattern because 1. its "root" node is a verb; 2. the verb has an "aux" child; 3. the verb is linked with another clause via a "conj" relation; 4. the other clause is headed by an adjective and has a "cop" (copula) dependent; 5. both clauses contain a "nsubj" (subject) and an "advmod" (adverbial modifier).

we currently do not check prepositions in English; instead, we manually fix sentences where prepositions are not compatible.


Later, we start the network construction by making an attempt to find a base word for each lexeme.

to the best of our knowledge, it is the biggest language resource of derivational morphology for Polish.


Although their approach is completely unsupervised, it requires POS tagging and, particularly for Spanish, it provides a considerably lower accuracy of extracted relations than for the other languages (73% for Spanish compared with 98% for English and German).

we have also visualized some parts of the Spanish word-Formation Network.


Polish WordNet contains many relations which store information about derived words such as "feminity" which links masculine nouns with its feminine counterparts, "inhabitant" which connects geographical names with the name of their inhabitants, "aspectuality" which relates verbs of different aspects and many others.

in recent years researchers noticed the potential of derivational morphology to improve the performance in many important areas of NLP, which caused the development of novel language resources which focus on word formation.


We store the corresponding wordform, category, morphological features, source lemma, UD lemma 5-tuples for later use.

each language makes use of its own guidelines regarding the inventory of categories and the detailed definition of morphological features and feature values.


In Table 2 we use the code "TRsource language" to identify lexicons created in this cross-language way.

apertium does include a morphological lexicon for Czech, a language closely related to Slovak.


Table 1 shows the a summary of the current resource sizes of selected languages, along with the number of distinct inflections covered, and the number of expanded phrasal glosses generated given multiple translations per lemma.

a full specification of the UniMorph annotation schema is available.


To correct these errors, we noted that for each part-ofspeech within a language in Wiktionary, authors use only a handful of distinct table layouts.

determine which entries in an HTML table are inflected forms and which are grammatical descriptors.


The verbal morphology of Upper Tanana is typical of a Dene language (Rice, 2000), and often involves a complex interweaving of non-continuous lexical, derivational, and inflectional prefix sequences.

second, and more importantly, this approach allows us to model significant aspects of the derivational system of Upper Tanana.


a lexical entry for a verb like na#D+kuyh 'vomit' (imperfective), which contains only the stem kuyh and a single lexical disjunct prefix na-, can be specified as na=kuyh, and subsequently expanded by the model automatically into na=_.kuyh.


Since the true tags are available neither in automatic nor manual annotation, we select 34 Polish words which are often abbreviated 4.1., look up their inflected forms in the Polimorf morphological dictionary (Wolinski et al., 2012), and gather sentences which contain at least one of these words.

a different branch of text normalization is focused on abbreviation discovery.


abstract goes always after title detection, bibliographic entries go always after sections' textual content).

effective tools to extract structured textual content from PDF files represent a key technology to enable scientific text mining (Ronzano and Saggion, 2016).


In order to provide experts with a web tool to process big data related to their domain, the TERRE-ISTEX approach was improved.

the MODS-TI data model expands the MODS format to describe spatial, temporal, and thematic entities extracted from documents.


The vocal (verbal and prosodic) annotation used in this project follows the principles previously developed for spoken Russian discourse, for more detail see Kibrik, Podlesskaja, 2009 and the website spokencorpora.ru.

in addition, the camera GoPro Hero 4 (50 frames per second and 2700х1500 pixels) was used to record the whole scene (see Fig.


Many prosodic phenomena are of a relative, rather than absolute, nature: their specific realizations can be assessed and identified only with respect to neutral characteristics of a given speaker's voice.

during an interactive stage (conversation), the Commentator supplied additional details and corrected the Narrator's story where necessary; the Reteller checked his/her understanding of the plot, asking questions to the Narrator and the Commentator.


Discourse structure analysis not only helps to understand the discourse structure and semantics, but also provides strong support for deep applications of natural language processing, such as automatic summarization, statistical machine translation, question and answering, etc.

in addition to the logical semantic structure, we also define the functional pragmatic structure.


The edges connect the discourse units, while the arrows pointing to the primary discourse units.

discourse structures vary if the genres are different.


Only 4 dialogues out of the 54 were held between subjects with the same mother tongue.

as we have shown in our preliminary analyses, this data offers various possibilities regarding the study of mechanisms that regulate human communication.


Despite the recent advances in the field of Spoken Dialogue Systems (SDSs), non task-oriented spontaneous dialogue is still a very challenging problem since its structure is often difficult to represent, unlike task-oriented dialogues which could easily be represented by a flow chart.

this JSON files contains all the dialogue ids and a link to a JSON set file.


Whenever the audio for the whole set was available, all the turns in the set, including those where participants are refining their strategy between scenes are included, with information about time boundaries, speaker, turn index, topic and rich transcriptions.

the scene sea ( Figure 11) has 9 objects in the XML description, but the entropy value in 3.99 (median value is 3.57).


The insight into these many works is that neural networks are better suited at capturing semantic clues between the two arguments of an implicit relation than traditional methods heavily reliant on feature engineering, as in (Pitler et al., 2009;Xue et al., 2015).

unlike these methods we make no feature engineering.


We run each experiment for ten times and take the average.

the hidden vector s t obtained after the last character is called the last feature vector, as it stores the information related to the character language model and the sentiment of the utterance.


This gives evidence that when we process the text of a conversation, we can see the context of a current utterance in the preceding utterances.

it was shown that the average vector over all characters in the utterance works better for emotion detection (Lakomkin et al., 2017).


(a) Visualization Modes Representing data by means of alternative modes helps in understanding and interpreting the subject matter.

any annotation can be revised during the actual session.


This task has been reproduced with variations on the language (Cholakov et al., 2014;Fabre et al., 2014) or the size of the dataset (Kremer et al., 2014).

by beginning to identify the main differences in terms of substitutes proposed by humans and NLP systems, we can complete the initial analysis proposed by (Tanguy et al., 2016) who found that there are also important differences in the difficulty encountered for specific target sentences.


For example, considering again sentence 208, the wordécartement, which is a morphological variant of the substituteécart, appears as a new valid substitute.

following (McCarthy and Navigli, 2009) we created a new annotation task mixing man-made and automaticallyretrieved substitutes.


This is another characteristic that explains why especially the more advanced classifier methods work better when trained on Gulli's and applied to Bernard.

yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $2.5 billion.


The DT classifier performs particularly bad especially for the cross-application task.

only basic NLP techniques (e.g., checking similarity thresholds over string-and n-gram-shingles), which are independent from an advanced global knowledge or training experience, are applicable for analyzing historical corpora (Büchler et al., 2010).


Wikipedia has very valuable translation texts since these translations were manually made by editors.

the article ends with some conclusions and future directions in Section 4.. For the creation of the proto-dictionaries, we applied several lexicon building methods utilizing Wikipedia and Wiktionary.


We also want to convert our data into the data format following the conventions of linguistic linked open data and provide them via our web site 3 or via the repositories of dictionary families such as Giellatekno 4 .

these methods need a large amount of (pre-processed) data and a seed lexicon which is then used to acquire additional translations of the context words.


In section 4, the evaluation results are presented and discussed according to different aspects.

the system behavior was studied and validated on different corpora and speech styles.


Our comparative analysis reveals the differences between crowdsourced data and system usage data.

the score of an utterance x is calculated such that the impact of the utterance length U L is normalized: the results are filtered by an experimentally determined threshold value.


Nevertheless, it is difficult to decide whether the scores they report are satisfactory for productive use.

we show that when training NLU services on crowdsourced data the scores achieved are as good as system usage data, even when the test set contains faulty utterances.


toward the optimal goal of human-level intelligence, CV researchers have actively studied on Visual/Video Question Answering (VQA) (Antol et al., 2015;Ye et al., 2017;Zhao et al., 2017), which is to understand textual questions and images and give correct textual answers by machine.


In the overall VCOPA dataset, we compare using machinegenerated captions with using human-generated annotations again.

as reported in (Luo et al., 2016), although CausalNet formally achieved 70.2% accuracy on COPa evaluation, its accuracy on the overlapped set between COPa and VCOPa is 67.9%.


The graphic relates the percentage of verb and noun pairs that were analyzed with the number of VNICs that were correctly identified by the models, essentially showing the gain calculations through each of the model's deciles.

because they focus on evaluation accuracy they are evaluating both the model's ability to score an instance appropriately and the threshold the model uses.


Most of the work in this field either uses accuracy (used by Fazly et al.

∼60% of our data is labeled as positive by our model (shaded green), while 40% is labeled as negative (shaded red).


An optional gaussian curve can be overlaid.

developed by the LNE, specialized in the evaluation of NLP systems, Matics is free and open-source.


MetaMap relies on a powerful tool to deal with variability -the SPECIALIST Lexicon; we do not address variability but for a closed list of abbreviations.

we also report a first approximation for assessing the performance of the prototype.


As it happens, MetaMap's knowledge base contains "congenital asplenia" but not "congenital anesplenia", and so it does not annotate it.

a qualitative error and disagreement analysis has been carried out in an attempt to elucidate these issues.


The datasets consists of a set of terms from different corpora and the statistics of the datasets are given in Table 1.

for example, it is common that large organizations will collect many documents generated by their staff into a content management system, with only limited organization such as tags.


For each word in a given term, we obtain a vector of fixed size K (we choose K = 100).

we can then assume that we can minimize the error by finding the point where we derive this using identities proven in the Matrix Cookbook (Petersen et al., 2008), readers are recommended to refer to this text to better understand the derivation here: : we can easily find a matrix V + that satisfies 5 V In particular this can be achieved by using the singular value decomposition of V in order to find Such that Σ is a diagonal matrix and the following hold It is clear that a solution to find A is: To obtain the A matrix, we used all but one of the taxonomies as training data and evaluated on the rest of the taxonomies.


In theory, RNN can keep a memory of previous information.

many approaches have been proposed in the post studies.


However, it was difficult to train RNNs to capture longterm dependencies because the gradients tend to either vanish or explode.

native speakers may associate a fully expanded term with its abbreviation by some intuition.


Nonetheless, we barely see studies that consider NFFs.

we extract long phrases and terms in popular Chinese natural language processing corpora, which include People's Daily corpora and SIGHAN word segmentation corpora.


As an example of annotation work, Figure 2 shows a part of a sample annotated document in the Korean TimeBank.

the relation should be RT2 instead of RT3 or RT4 due to the relation's range.


As noted above, annotations registered in PubAnnotation are aligned with the canonical text and all other annotations applied to the same data.

the LAPPS Grid seeks the contribution of tools and resources for biomedical text mining to augment the current facilities.


Each of these was run three times (once each with T, TD, TDN queries, each created by concatenating all words in the indicated fields).

as Table 3 shows, assessors a and B show reasonable consistency in system ranking.


The FrNewsLink corpus allows addressing several multi-modal linking tasks, with heterogeneous data from various sources and of various length.

then, a manual annotation process (described in Section 4.)


The obtained MAP@10 in such configuration is 83.7%.

of the linking annotation process, TVBN topic segments can be separated into two sets.


Annotated data are then used for training the K-Nearest Neighbors algorithm (KNN) classifier.

this method usually increases the contrast in the image.


Crucially, the weights depend not only on the Euclidean distance of pixels but also on the radiometric differences.

the AM can be adapted using the available recordings to fit the actual acoustics conditions in the interviews.


More specifically, Katz and Frost (1992) introduce the notion of orthographic depth.

in fact, the positive correlations of the acoustic weighting factors seem to support such an interpretation.


• grab strength: float, the strength of a grab hand pose as a value in the range [0, 1].

the gestures are with defined meanings that are independent of speech.


The information gain from multimodal features was not relevant for classification in our corpus since there is too much noise in certain categories (eg., in Figure 4 in which a printer, a cable and a sensor were grouped together in the same category).

the objective function of this model reflects this ratio in the distances between these word vectors.


It uses simple metrics and processing based on object and event semantics.

in the context of shared physical tasks in a common workspace, shared perception creates the context for the conversation between interlocutors (Lascarides and Stone, 2006;Lascarides and Stone, 2009b;Clair et al., 2010;Matuszek et al., 2014), and it is this shared space that gives many gestures, such as pointing, their meaning (Krishnaswamy and Pustejovsky, 2016a).


The final entailment label is actually a pair of two labels: • entailment+neutral points to one-way entailment, 19 • entailment+entailment points to equivalence (two-way entailment), • neutral+neutral points to no entailment.

each sentence pair is human-annotated for relatedness in meaning and entailment.


; 4. part-of-speech tags, automatically generated with the Tree-Tagger (Schmid, 1995) and manually corrected; 5. lemmata, automatically generated with the Tree-Tagger and manually corrected; 6. the information which object is moved and where it is moved to (manually annotated); 7. the information whether the left or right hand touches a particular object (manually annotated); 8. the information whether a particular object touches the ground/table (automatically identified by the object tracker and manually corrected); 9. segmentation of the stream of words into chunks using heuristics such as long pauses (min.

the overall goal of the data collection activity was to gather multimodal data of basic actions such as tAKE (Ge: nehmen), PUt (Ge: stellen/legen) and PUSH (Ge: schieben).


Table 1 contains the number of different anomaly induction methods used during the creation of the corpus.

speech signals correspond to the same text: "Implorant le pardon de sa fille, il se mità aiguiser sa hache" (Imploring his daughter's forgiveness, he began to sharpen his axe.).


As far as we know, no emotional audiovisual corpus exists containing controlled, acted or natural anomalies to address specifically the anomaly detection question.

they are extracted by using the PRAAt software (Boersma and Weenink, 2016).


The reason could be that reactions caused by induced anomalies are more subtle and nuanced, sometimes under control of the speaker, and then more difficult to detect than acted behaviors.

in this paper, we present a new and complementary multimedia corpus called EMOLY which contains human-centered anomalies.


Online archives of presentations provide valuable sources of material for study and research.

video segments were aligned into time windows each 90 seconds in length, i.e.


The treebank was divided into a training set of 5639 sentences and a test set of 1270 sentences for learning and testing POS tagging and dependency parsing.

other treebanks are built manually for languages such as Norwegian (Solberg et al., 2014).


The lowest precision is 62.0% and the highest value is 66.8%.

an automatic classification of complements for each verb in any sentence would solve this sophisticated problem.


The project is managed using the maven build automation tool, giving researchers two simple ways to start using the framework.

the German sentence is automatically annotated with multiple layers of linguistic annotation.


In recent years, different language processing applications demand state-of-the-art parsers.

in this study, an attempt will be done to create treebanks for Amharic.


Treebanks have been developed for well-resourced languages in different frameworks such as Phrase Structure, HPSG, and Dependency.

we have mentioned problems related to clitic segmentation and indicated that Amharic orthographic words may not only bear morphological information but also carry other function elements of syntactic relations.


In Arabic and Hebrew, such instance is treated as agreement phenomena within the noun phrase.

functionally, converbs may have three functions: serial, consecutive, and co-extensive (Meyer, 2011).


When adopting UD, we need to give language-specific information regarding the POS tag-set relevant to Amharic.

amharic is a less-resourced and morphologically-rich language where problems of OOV and ambiguities are major bottlenecks.


Syntactic dependency types for Amharic are defined in order to be as consistent as possible with the principle of UD.

we consider them as non-main verbs and the final verb as a main verb.


The nominal will be given the grammatical role of nsubj, obj, etc., while the clitics will be treated as a pronominal copy of the nominal and will get the role of expl.

the distinction they want to capture by the tags of the subcategories will not be used when such forms attach a preposition.


Google Cloud Platform (GCP) (Google, 2018d) has various container solutions, two of which are used by our system: 1.

ttS engines and synthesizers are hosted in containers via Docker images.


These new words are extracted from many language resources on the Web automatically or semi-automatically, and it is frequently updated (currently twice a week).

it is common to conduct text formatting, sentence segmentation, and character normalization as pre-processing.


This paper proposes a visualization system for chemical compounds.

this paper proposes a visualization system for chemical compound information extracted from Japanese texts and chemical compound databases.


The processed information is embedded as links in text.

in a similar way, 'Acrylic acid 4 -(1,1dimethylethyl)phenyl ester' is split into 'Acrylic acid', '(1,1-dimethylethyl)' and 'phenyl ester'.


We expect that a possibility to access useful information would increase by using these rules.

figure 2 shows an example of an extraction of a paraphrase rule from the above two chemical compound names.


The skill of reading is less dramatic with around 40% of students expected and actually measured at the lower 2 competency levels.

more background information to this problem is already presented in the introduction of the precursor paper at LREC 2016 (Berkling, 2016), which introduced the H1 Corpus (Linguistic Data Consortium, 2016).


According to an investigation conducted by the Japanese Ministry of Internal Affairs and Communications 2017, 96.8% of people in their 20s connect to the Internet on their smartphones.

moreover, teachers are at risk of failing to record observations and students may not be able to conduct their observations through playback of the video data.


I would like to thank Professor Atsushi Mori for his helpful suggestions.

it is feasible to use smartphones as devices for FWM in the classroom, in particular if a small number of auxiliary smartphones can be secured.


28 Though we occasionally already use this feature, we would like to make it an integrated feature of the Alpino extension to the TrEd editor; (3) The CHILDES annotations and information in tiers related to an utterance such as in example (1) are currently ignored by the cleaning program.

text corpora in which each utterance is assigned a syntactic structure) for language acquisition data, in particular the Dutch CHILDES corpora (MacWhinney, 2000).


We assume that children converge on a grammar structured like the grammar for adults and actually almost identical to it.

without an overt subject it can occur as an infinitival or participial complement to other verbs in the adult language.


We will also provide the treebank in the CHAT format, in the MOR and GRA tiers (MacWhinney, 2017).

we specify that the properties of the top node of the parse tree containing the selected elements (smain, i.e.


We used the American database to investigate which indexes are good measures to evaluate noun predominance: the comprehension days, the production days, or the C2P indexes.

relational relativity insists that a verb's meaning is not isolated and that it depends on the surrounding words.


The average number of comprehension days for verbs was 480, and for nouns it was 498, showing a small difference in the number of days between them (p <0.2).

we found that for some words, the comprehension acquisition rates were not appropriately calculated since we didn't have as much data for these words as for the word production data.


In this training phase, learners perform a shape/word matching task.

after many years of research (Neri et al.


If they had clicked on an incorrect shape, it is indicated with a red frame.

speakers of a free-stress language need to encode stress position in their mental representation of the words.


Therefore, learner answers can optionally be normalized before feature extraction or language errors are simply annotated to be consumed later by a feature extractor that extracts the number and type of errors in an answer as features.

for example, their are lots of websites that offer online tutoring or good ways to help you pass school.


In this paper, we present a reading corpus in MSA collected from textbooks of the United Arab Emirates (UAE) curriculum and a collection of 129 unabridged works of fiction.

we exclude punctuation and digits (non-content tokens) from our calculations, which make up 18% and 15% of all tokens in the curricular and fiction subcorpora, respectively.


Before certain endings, the final -a-remains short, while before other endings it lengthens to -aa-.

the suffixes trigger morphophonological changes in the stem, participate in blocking, and exhibit variable ordering in certain constructions.


Text is of course also essential for NLP, computational linguistics, and other research uses of corpora.

a dictionary augments the HMM by contributing additional language knowledge, and a human annotator provides judgements in a small subset of cases that are identified as otherwise most prone to inaccurate output.


The Faroese dictionary is from an open-source spell checking project; although Faroese is spoken by a small population, it is a living language whose speakers have one of the highest rates of computer/internet usage globally.

incorporating a moderate investment of annotator time during post-processing leaves an acceptable token error rate below 1.5% in this low-resource setting.


Whereas the first motivation is language-internal, the second motivation is external, as it contributes to our deep understanding of natural languages.

functionalists reject the idea of arguments/adjuncts dichotomy in the first place; Haspelmath (2013) called pronouns/nominals as cominals and claimed that in these cross-indexing constructions both are real arguments .


Beginning with a Filemaker Pro database (Johnston, 2001), the Auslan lexical database has been developed as a resource to inform the study of Auslan and the annotation of a corpus of Auslan video recordings (Johnston, 2008).

the current version of Auslan Signbank is an open-source re-implementation of the original website, with features added to allow updates to the database by researchers.


For example, 転 移 (metastasis) can be associated with 転移性腫瘍 (C80, Malignant neoplasm, without specification of site), and also 止血困難 (difficulty in hemostasis) can be associated with 出血 (R58, Haemorrhage, not elsewhere classified).

the coders tried queries that are created by omitting modifiers in the name.


Step 1: preprocessing In the first trial, we simply put the output of the morphological analyzer JUMAN into word2vec, however, the result was unsatisfactory.

for each compound verb, the verb and verbal phrases were arranged in descending order from the highest score.


Moreover, it will allow us to classify the resources generated (i.e.

access to corpora was regarded essential and desirable in the same proportion, whereas access to MT engines was deemed only desirable.


Section 5 covers the previous work related to idioms, sentiment lexicons, and sentiment analysis.

in the case of possessive adjectives, we replace occurrences of one's, anyone's, or someone's with my, your, his, her, our, and their.


The average correlation of the annotators was 0.59 while the average correlation with the final average ratings was 0.8.

• As the dataset was intended to test language models, each word should be translated into one word.


Using the NodeJS 6 library to run the server and the backend parts.

these issues are of course endemic to software development and until recently could only be solved by integrating all components within a single development model, for example, such as integrating all NLP tools using the Python language as has been done by NLtK (Bird, 2006).


Previous NLP platforms had multiple issues related to their architecture (ability only to apply one task at a time), user experience (no graphical user interface), or interoperability.

the platform exploits the Semantic Web model of data types, to describe the possible format of input and output to services.


Section 5. illustrates the annotation tool.

their content can be automatically mined but there are still limitations.


The export must be dynamically configurable, so that the same NLP pipeline can drive the generation of different triples in the same, or multiple different, knowledge bases.

this approach supports agile data science workflows (Sateli and Witte, 2016), where existing NLP pipelines can be easily connected with different web vocabularies, for example, to facilitate submissions to shared tasks within competitions.


In order to enrich the LiDo RDF dataset with external data sources and simultaneously provide the possibility for other dataset creators to point to linguistic concepts or terms within LiDo RDF, an integration of the data into the LLOD Cloud is considered a worthwhile undertaking.

term data: term instances can have additional etymological information or a given abbreviation (which are not shown in Figure 1).


To the best of our knowledge this manageable and semantically standardized set of relations is unique for a dataset of linguistic terminology because these more specific relations can be used to disambiguate the sub-and coordinating relations that are normally only formally defined and, hence, confound semantically different relations.

data reuse for human users: RdF datasets are generally not easily to be read by humans.


The dataset versions and all the features described will be available from http://lidordf.aksw.org/.

a manual set of 50 links adhering to the requirements of publishing datasets within the LLOD Cloud 23 has been created as a starting point to add LiDo RDF to the already existing collection of linguistic data within the Cloud.


from raw (ATF) text to (CoNLL)RDF-compliant corpus representations.

this includes the major administrative and legal Sumerian corpus of the Ur III period (2100-2000 B.C.)


This will be followed up by actions concerning the setting up of a repository of language resources for CEF AT and further and gathering language and translation data, PMKI platform aims first to harmonize multilingual language resources making them interoperable, then to integrate supplementary public multilingual taxonomies/terminologies in a standardized representation.

pMKI aims to share maintainable and sustainable Language Resources making them interoperable in order to support language technology industry, and public administrations, with multilingual tools able to improve cross border accessibility of digital services.


In the 20th century, a vivid literary production developed, and the language was taught in schools and used as a medium of mainstream communication.

abkhaz, a polysynthetic language, exhibits a high degree of morphological complexity and word form homonymy.


The ELRC action shows that public sector institutions are very interested in using MT and they have ample resources to contribute.

instead of providing language data, several institutions pointed to their websites with parallel multilingual content.


For instance, the English idiom "Once in a blue moon" means "something that happens rarely" and another English idiom "When pigs fly" means "something that will never happen".

for example, Listing 4 shows a SPARQL query for retrieving a given part-of-speech tag of the English idiom "out of the blue" from the same resource exists in DBnary.


Note that, all data sources are bilingual but not necessarily including English as one of the involved languages.

a word, a multi-word expression or an affix), (2) the sub-class ontolex:MultiwordExpression is used to specify a lexical entry as a multi-word expression, (3) the ontolex:LexicalConcept class suits perfectly for representing idioms meaning as its formal definition comprises of "to be a mental abstraction, concept or a thought that can be described by a given collection of senses".


As factuality tags, we use factuality values proposed in FactBank (Sauri, 2008).

eVa With wish, request, command or obligation.


In modern Chinese, Light verbs are generally defined as the semantically bleached verbs in the sense that the predicative content mainly comes from its taken complement (Zhu, 1985) while the light verb itself may only serve as a syntactic operator, without containing any eventive information.

finally, we have chosen Chinese Universal Dependencies treebank 1 (wiki data) because it features syntactic annotation in dependency-based format required by the shared task.Unlike other languages such as Czech (Bejček et al., 2017), there is no resources we can use to generate MWEs automatically for Chinese.


Test 4 is a syntactic test, aiming at testing whether a NP in which verb's subject becomes a noun's dependent evoke the same event.

if the light verb can be omitted without changing the proposition/semantic meaning of the construction, we consider the verb itself adds no semantic and is semantically bleached.


In many cases, a noun phrase follows this type of MWEs.

(2) Annotators live in countries with English as an official language.


Whereas Spanish is a romance language, Basque is a non-indoeuropean language which does not belong to any known family.

in Handbook of Natural Language Processing, Second Edition, pages 267-292.


Finally, in most cases the PROCESS + PROCESS pattern was found to encode the relation causes in weather-or waterrelated processes (e.g.

on the one hand, verb paraphrases (Nakov and Hearst, 2006) can be used (e.g.


As stated earlier, although hyponymic CNs usually have the same external form, they can encode different semantic relations.

all too frequently they show similar external forms but encode different semantic relations because of noun packing.


To validate the generated error corpus, we carried out some preliminary experiments on collocation error recognition and classification, using LSTMs, in which we achieved an average precision of 0.95 and an average recall of 0.67.

a list of irregular determiners together with number inflection rules is used for the former, while a suffix map is used for the latter.


The results showed that using our processing methods can still produce a tagger with an f-measure score of 0.95 and higher.

figure 6: An illustration of the deep convolutional encoder with depth 1.


The first lines define the UIMA data reader: one for the training data and one for the test data.

one The beautiful tree … DET ADJ NOUN ...


Based on previous successes with this method for classification tasks in Twitter, we hypothesize that similar ensemble methods with neural networks using different weight initializations could also produce improvements for the tasks of hate speech detection in Twitter.

we conclude that with 99% confidence, our ensemble method will perform better than an individual model 98% of the time.


aBSa entities and attributes were automatically mapped to these labels as follows: • LaPTOP DESIGN_FEaTURES and RESTaURaNT STYLE attribute labels (used for all the texts that include a reference about specific features such as size, color, presentation, styling, ambience, etc.)


Note that brand names were also ignored and were not selected for the BoW if recognized.

our data showed that it cannot be explained in terms of word frequency or distance to the question word, as suggested.


In our case, default words represented 43% of the errors.

for short text classification, this approach delivers very sparse vectors, which are not useful for classification purposes.


In this work we address the issue of computational author profiling from a Facebook corpus in the Brazilian Portuguese language.

existing studies of this kind are often focused on age and gender prediction, and are in many cases limited to the use of English text and a few others.


Table 3 shows that RDRsegmenter obtains the highest F 1 score.

on the benchmark Vietnamese treebank (Nguyen et al., 2009), experimental results show that our method obtains better accuracy and performance speed than the previous state-of-the-art methods JVnSegmenter (Nguyen et al., 2006), vnTokenizer (Le et al., 2008), DongDu (Luu and Kazuhide, 2012) and UETsegmenter (Nguyen and Le, 2016).


This research was partially supported by the Australian Government through the Australian Research Council's Discovery Projects funding scheme (project DP160102156).

each syllable is labeled by either segmentation tag B (Begin of a word) or I (Inside of a word).


Hasanaj (2012) presents two tagsets: A basic set that consists of 16 tags and a large set that consists of 326 tags.


Albanian has a rich morphological system and a relatively free word order, similar to, for example, German.

they are certainly more grammatical in nature and written as two separate graphical tokens.


In this class, the lemma has a plural meaning, and the singular is derived from the plural by adding a morpheme (taa marboutah) in the end, such as: baqar "cows", *ubAb "flies", tuf AH "apples" (rationality: irrat).

there are two main concerns with the LDC data (Marcus et al., 1995;Maamouri et al., 2003;Lander, 2005) that poses somes questions about its reliability and relevance to contemporary language.


UniMorph resources are rarely original resources, but rather extracted from existing material, 6 such as Wiktionary (Kirov et al., 2016, first-generation UniMorph inventories) and other dictionaries, bootstrapped from morpheme inventories or corpora (as described here), or generated by rulebased morphologies.

the bird up there is flying away from near that mountain.'


We believe that a corpus like EMTC will greatly benefit the development and evaluation of emotion analysis systems and improve their ability to express and interpret emotions in text-based communication.

we can also easily eliminate the low inter-annotators agreement score problem that is often encountered in other corpus (Strapparava and Mihalcea, 2007;Dini and Bittar, 2016) by providing them the clips from the movies in addition to the transcripts ( Figure  1a).


Human have developed language to fit their needs of expressing ideas and feelings.

we would like to investigate the correlation among annotated labels and expand the size of testing data of our corpus using the same annotating scheme in the future.


For example, in "rienà voir avec le seigneur des anneaux, carrément passionnant" [(this film has) nothing to do with the Lord of the rings, (that is) downright fascinating], (1) the phrase "downright fascinating" indicates a very positive opinion, but it is applied to another movie: "the Lord of the rings".

after the choice of the annotation form and the redaction of the guidelines, experiments have been conducted to estimate inter-annotator agreement.


If a word or phrase is found there, the core lexicon will not be searched for that item, and its information in the plugin lexicon will be used.

bing Liu Opinion Lexicon (Hu and Liu, 2004).


Our context rules work by specifying words or phrases that can appear in the immediate vicinity of the identified sentiment word.

plugin lexicons can be used to provide domain-specific sentiment items, but also to override polarity assignment in the core lexicon, for whatever reasons.


we place a strong emphasis on such behaviour by the users and translate the tweets accordingly into the target language, the result of which is essentially informal in nature as well.


The objective to translate the tweets using sentimentspecific translation models and at the same time preserve the sentiment during translation.

the sentiment-annotation procedure is explained in Section 4.


The ID is the unique identifier within the corpus and the source ID is the message identifier of the microblog platform.

the data were then passed through further content filtering operations (to exclude spam and unreliable authors) which are described below.


Texts are short and include many references to concepts that have a specific terminology.

average interannotator agreement across all categories is 0.68.


Aspectbased sentiment analysis deals with identifying sentiment expressed towards different aspects or dimensions of an entity.

the two annotators are not told about the claim to be verified, and are not told that one of the pair is a tweet and one of them a book snippet.


General Feedback means that feedback is taken for every aspect of the lecture by asking a question such as "Give feedback on today's lecture".

this nature could be a characteristic of south Asian cultural background.


To resolve the matters in ambiguity while annotating the dataset, we had to come up with a hierarchy based on the entity-aspect relationship to select the best suitable target to annotate.

we carried out contextsensitive spell correction ("Microsoft Cognitive Services-Bing Spell Check API | Microsoft Azure," n.d.) on the corpus .


The two latter settings classify questions as NISQ only if more than one of the indicators are present in the questions.

for the questions, the annotation field contains the final score that was automatically assigned across the four languages and our annotation based on that score and on setting 1, as presented in Section 4.


Suggestive questions are used to imply that a certain answer should be given in response (Don't you think that eating chips is unhealthy?).

as mentioned in the introduction and as discussed in the conclusion, our approach can be seen as a first step for annotating large, parallel datasets so that these can be further used as training data for machine-learning approaches.


Along with the techniques illustrated above, after the initial data generation, they also propose to anonymize all named entity tokens in the data to avoid the model exploit world knowledge of specific entities, increasing the difficulties in this dataset.

in this paper, we propose a new Chinese reading comprehension dataset for the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017), consisting large-scale automatically generated training set and humanannotated validation and test set.


They also generate the data in an automatic manner, which is similar to the previous datasets.

existing reading comprehension datasets are mostly in English.


We provide a corpus of explanation graphs for elementary science questions suitable for work in developing explainable methods of inference, and show that the knowledge frequency, explanation overlap, and tablestore growth properties of the corpus follow predictable relationships.

might need only take the form of explaining that one of bark's primary functions is to provide protection for the tree.


The tables most frequently used to author explanations are shown in Table 3, broken down into three broad categories identified by Jansen et al.

if an inference method requires 3 or more rows, this likely would not be possible without a corpus of at least 20,000 questions and explanations -a substantial undertaking.


floor plan 10 C I prefer a one-bedroom apartment.

the average number of the target utterances in a dialogue is 23.9, and the average length of the utterances is 19.8 characters in Japanese.


Their study contrasts H-H and H-A Interaction corpora on a negotiation task over a definite set of objects (based on the H-H and H-A negotiation corpora (Gratch et al., 2016), unfortunately not publicly available).

participants were asked to read the two first pages of Alice's Adventures in Wonderland, before having a 7 minute conversation with the 'virtual expert'.


This leads in autonomous agents to social signal processing and speech recognition with low accuracy and thus to an agent that does not understand the user.

in this work, we aim at employing the same measures on the Alice corpus, to study the verbal alignment process in H-A interaction on an information-providing task driven by a sophisticated WOz able to adapt to the user.


To the best of our knowledge most state-of-the-art agents cannot yet cope with interruptions effectively, nor perform human-level verbal alignment (Dubuisson Duplessis et al., 2017b).

while easy to implement and robust in simple question-answering applications, this approach sometimes selects a 'wrong' answer (see for example (Schooten and op den Akker, 2011)).


The wizard had immediate and almost full control over the agent's verbal and nonverbal behavior, as the wizard provided the agent's speech through his own voice and his facial expressions were directly copied onto the agent.

in a classic WOz approach where the wizard uses a button interface, it is nearly impossible to improvise in unexpected situations.


For example, referring to the gender of the user after misclassifying it.

this alignment is only one-way: the system is usually not able to align on the user.


There are two differences between their work and our framework: using a multi-modal dataset and applying a supervised manner to both the QA model and QG model.

our method decreases the test error by 1.24% in the case of the annotated data of size 75K.


As the advent of deep learning techniques turns algorithms data hungry, building resources of a significant size is a critical task, question answering (QA) systems, as demonstrated not only by SQuAD, but also by ImageNet for object recognition (Deng et al., ).

our key contribution is to show that Seed does not have to be big and annotate such data ourselves.


Unlike search engine instead of extracting information, here a QA system usually focus on extracting relevant and precise answer(s).

proximity score (pS): It calculates the length of the shortest span that covers the query contained in the candidate answer sentence.


the categories that co-occur more frequently with HS are, expectedly enough, stereotype (72% of cases), aggressiveness (66%) and offensiveness (51%) 6 .


in the analysis of intensity degrees and their distribution, we thus focused on these aspects, so as to better understand whether an interdependence among all these categories actually exists and, ultimately, to come up with a "data-driven" definition of HS based on these findings.


For example, the user in Table 1 frequently accesses the IMDb and Spotify services, and he/she is also a follower of the IMDb and Spotify Twitter account.

on average, we collected 6 interests per user.


Spotify is among the services allowing to generate self-generated content shares in Twitter.

as mentioned in Section 1., semantic techniques can be applied to reliably identify the main categories of interest for each user, an enhancement that we leave to future work.


When we restrict the above analyses to only the "personal" Fitocracy and Reddit contexts we find no gender-based differences in response relevance (b=0.01, p=0.87), and the magnitude of the emotiveness difference is greatly reduced (b=0.11, p=0.046).

since important forms of bias (e.g., dehumanization or treating a person as their social category) often happen at the level of individual responses, responses to gender are an understudied but critical lens for studying gender bias.


(Meftah et al., 2017) showed that an initialization with a combination of several pre-trained embedding vectors (from different pre-trained models) improves significantly the performances.

word vectors (the combination between character level embedding and word level embedding CNN) are fed into a 100 dimension Gated Recurrent Units (GRUs) layer, a variant of RNNs.


Cross-task TL performances In order to understand how cross-task TL improves POS tagging performances on Spanish, German and Italian social media content.

in a TL scenario, the knowledge learned by handling one problem is used to help solving different but related problems.


To learn orthographic features at the character level, we use a CNN architecture similar to that of Ma and Hovy (2016).

each word in the input sentence is represented by a combination of two vectors: 1.


We set the character embedding dimension at 30, the dimension of hidden states of the GRUs layer at 100 and fully connected layer (FCL) dimension at 80.

in the case of social media content, it is difficult to achieve the performances of state-of-the-art models based on hand-crafted features by applying neural models trained on small amounts of annotated data.


A node with more paths to positive than negative seed nodes will most likely get a positive sentiment value.

to be able to create a sentiment lexicon based on raw tweets, about 400 million tweets were downloaded using the twitter Streaming API 1 in the period 18/12/2015-19/03/2016 (92 days), with about 4 million tweets downloaded each day.


While previous work building PMI lexica have been based on rather small sets of tweets, the present work is based on over 100 million tweets, while still being highly efficient.

from Table 8, we can clearly identify all of the top 10 positive lexicon entries as actual positive phrases and the top 10 negative as negative phrases.


A detailed review is provided in (Fernández Gallardo, 2016).

questionnaire items were retained when the main loading was greater than 0.5 and the difference between main loading and cross-loading exceeded 0.2.


The NSC data facilitates the research necessary to clarify the relationship between "Distal Cues" and "Proximal Percepts", which should lead to machines reaching the human performance in the attribution of speaker social characteristics.

the newly acquired NSC corpus presents: • Clean speech recordings made in the acousticallyisolated Nautilus room (which gives name to this database) with the high-quality AKG C 414B-XLS microphone.


Errors of the DEEP tracker were exceptionally low, but the comparison with the remaining three trackers is not fair, since the test set was much smaller for DEEP (see section 4) and DEEP was trained to VTR-TIMIT and therefore has an advantage.

most of these earlier studies are in agreement that the quality of formant trackers' results can be improved by adjusting the parameters of the algorithm to the given task, i.e.


The formant tracker PRAAT showed systematic errors with male speakers, while SNACK and ASSP performed better on male than on female speakers.

this interpolation facilitates an evaluation of formant tracker output independently of the voiced-unvoiced detection of the tracker algorithm (because for every time frame of the recording there exists a reference value); on the other hand the resulting quality measure might be compromised: if a tracker is 'conservative' in the sense that it produces output only for the parts of the input signal where it is quite confident (clearly voiced parts), then this tracker will outperform other trackers who produce results in parts of the signal where the tracking is compromised for instance by creaky voice or noise associated with consonantal constrictions etc.


Formants in unvoiced or silent parts of the signal were either interpolated linearly from the two adjacent voiced parts or horizontally extended at the initial or final voiced portion of the recording.

when comparing the average RMSE error value for female speakers in PRAAT (194Hz) with male speakers in SNACK/ASSP (234/177Hz) we can see that the performance was in the same range (underlined values in Table 2).


This module can simulate: • various background noises that last for longer periods of time and overlap with the speech • short noises like the sound of the transceiver switching on/off, various on-board messages and beeps, etc.

actually, the ultimate goal of the research effort of our team was to develop a voice dialogue system simulating the responses of the pilot that could be used for training aspiring air traffic controllers.


South Africa has 11 official languages and since the majority of South Africans are multilingual, code-switching occurs commonly and spontaneously.

the distribution of code-switch examples is sparse, which could make modelling of unseen code-switch events challenging.


This means that our existing ASR already recognises such segments correctly.

edited transcript is a written document, thus it contains only final, grammatically correct and re-formulated sentences, which makes it not 100% accurate.


Later we will add these segments directly to the training data, so each segment should be reasonably short and contain speech only from one speaker.

the improvement from adding these sequences to the training data will be limited because existing ASR already recognised them correctly; they already match the acoustic model quite well.


So, we wanted to create a web-based application going forward that could potentially be accessed by anyone in the country.

in the case of IE, we face the issue of not knowing which native language(s) should be chosen for adaptation.


Metadata for manual annotations includes amongst other information annotator ids 7 , details of manual curation as well as applied annotation guidelines.

in an adjudication step all three annotators then decided on the annotation, and remaining hard cases were discussed in the project context and documented separately.


Eckart and Gärtner (2016) for a motivation and additional details of this additional layer): Based on the audio signal, some features of orality were re-introduced.

the interviews furthermore contain coreference chains and bridging links.


Cognition The identification of Cognition is charactized by the importance of features such as the ratio of questions.

we focus on elements such as pauses, social noise, disfluencies, but also on the vocabulary used.


Results revealed that the best performance is obtained when English is used for both training and testing.

results are presented in terms of percentage Equal Error rate (EEr), calculated using the standard NIST software (Safavi et al., 2012).


This study investigates the effect of different languages on the performance of speaker verification systems.

in the cases which the users are only capable of speaking their mother-tongue, it is best to use that language for both training and testing.


For this evaluation, we conducted 10 training epochs.

base, emotional change information of the event sentence readers is also associated with the sentences.


In recent years, the importance of dialogue understanding systems has been increasing because interactive interfaces handling a natural language such as smart speakers have become popular.

we think that these relationships are also important for understanding speakers' motivations in conversations (especially in emotional conversations) in addition to the relationships used in Hasegawa et al.


After the task, for each trigger utterance, overlapping replies and extremely low-quality replies (such as copied and pasted trigger utterances and empty replies) were discarded.

the example shown in table 1 presents typical acquired data.


There is also a corpus based on a collection of logs extracted from Ubuntu-related chat rooms that is mainly composed of technical support conversations (Lowe et al., 2015;Lowe et al., 2016).

judgements on the reasonableness of the 23,196 dialogues were acquired.


Following Jeffersonian transcribing system (Jefferson, 2004), we transcribe video-recordings of physiciancaregiver conversation to capture both what is said and how it is said.

intervention measures such as providing physicians with trainings of communication skills to resist caregiver pressure are likely to produce desirable outcomes.


We also took bust (chest, shoulders, and head) shots of each participant (recorded at 30 Hz).

in an experiment, we found that our estimation model using dialogue acts outperformed those using morpheme information alone.


They represent moreover the most difficult part of the system to be developed.

this platform, as described in Section 4, is semiautonomous: some modules of the architecture are simulated by an experimenter.


The way doctors deliver bad news related to damage associated with care has a significant impact on the therapeutic process: disease evolution, adherence with treatment recommendations, litigation possibilities (Andrade et al., 2010).

different gestures of both doctors and patients have been annotated: head movements, posture changes, gaze direction, eyebrow expressions, hand gestures, and smiles.


This offers an optimal sensorial immersion of the user.

our objective is not to learn a model (in a machine learning point of view) but to extract automatically and manually information to model the virtual patient's behavior (Porhet et al., 2017) that, then, will be validated through perceptive studies.


We extracted the lexical features based on Google's Word2Vec (Mikolov et al., 2013).

the difference occurs because utterances in a tV drama form a conversation that is affected by the story's settings, characters, and language style.


In Figure  4, we displayed an example of complex queries, in which the right order of operators "per date" and "highest" must be inferred.

quest has been evaluated against a complex corpus, which includes both SLqs and NLqs.


Of these, 335 are removed in both the manually-coded text and by the constraints.

as above, constraints are specified for animacy, number, and obviation combinations, as well as whether the verb upon which a nominal is dependent is to its left or right.


BPEmb and code to replicate our experiments is available at https://github.com/bheinzerling/bpemb.

recently, based on the assumption that a word's meaning can be reconstructed from its parts, several subwordbased methods have been proposed to deal with the unknown word problem: character-based recurrent neural networks (rNN) (Luong and Manning, 2016), character-based convolutional neural networks (CNN) (Chiu and Nichols, 2016), word embeddings enriched with subword information (FastText) (Bojanowski et al., 2017), and byte-pair encoding (BPE) (Sennrich et al., 2016), among others.


FastText performance shows the lowest variance, i.e., it robustly yields good results across many different hyperparameter settings.

averaging a mention's associated embeddings is the worst architecture choice.


This is expected for character-based models, but somewhat surprising for tokenbased models, given the fact that averaging is a common method for representing mentions in tasks such as entity typing (Shimaoka et al., 2017) or coreference resolution (Clark and Manning, 2016).

bPEmb and characterbased models show higher variance, i.e., they require more careful hyper-parameter tuning to achieve good results.


For a review of the main challenges in the field, see (Krahmer and van Deemter, 2012) and (van Deemter, 2016).

monologue and dialogue situations of communication may not be entirely comparable or, at the very least, may elicit different referring expressions.


However, studies of this kind will often rely on large collections of pre-recorded linguistic examples produced by every single speaker of interest, and on every domain under consideration, to obtain meaning-to-text mappings from which the lexicalisation model is built.

for simplicity, in what follows we shall focus on the choice of words that realise input properties, and we will leave aside issues of linearisation, agreement and others.


The inclusion of the context identifier id is intended to reflect the practical observation that a concept may not have exactly the same meaning (and therefore not necessarily the same wording) in different contexts.

a post hoc analysis suggested that the use of personality information is particularly helpful in the lexicalisation of affective information (e.g., properties conveying attributes such as smile, emotion etc.


REG involves two distinct issues: content determination (or deciding what to say) and surface realisation (or deciding how to say it in a given language).

a set of 368 descriptions of interest was obtained.


The attention model tends to produce more specific descriptions and therefore is likely to be given lower scores.

we performed a quantitative analysis of results based on four evaluation metrics, including BLEU (Papineni et al., 2002), a precision-based evaluation metric used in machine translation.


In less than two months, they successfully adapted the system to generate syntactic trees for wedding ring descriptions, speech pathologist reports, weather forecasts and flight details in French, soccer game descriptions in English, and lexicographic illustrative sentences for the lexical field of emotions in Mandarin, all with very satisfying results.

one must be able to control what the theme/topic of the sentence is.


It outperforms both systems (SBMT and Dress-LS) by the number of sentences which undergone at least one change (97.1% instead of 82.9% and 67.1%, respectively) and by the simplicity gain (+0.28 as opposed to +0.03 and +0.14, respectively), while achieving similar grammaticality of the output (4.34 as opposed to 4.28 and 4.27, respectively).

and lastly, we note the importance of keeping a reduced size of the vocabulary -no more than 50,000 words.


6c NTS-SARI-cross As a result, many mosques will not enforce violations, both men and women when attending a mosque must follow these guidelines.

more explicitly, we model the hypothesis number as a hyper-parameter that we select after the model finished training.


That is probably the reason for the much lower number of NE errors by the NTS systems trained and tested on the Wikipedia dataset.

5b NTS-SARI-cross Graham attended Wheaton College from 1939 to 1943.


Statistics regarding the size of the Wikipedia datasets are rendered in Table 3.

those with more experience were given more challenging training.


270 mentions), refer to generic uses of shooting and killings.

this is not the case for all expressions in the GVC.


For example, the predicate dbo:knownFor, which Portuguese label is "conhecido por", has the first word identified as an inflection of the verb "conhecer" (en:know).

if this sentence was translated into English, we would have indicated to whom the dictionary belonged, her or his.


Moreover, RDF has demonstrated a promising ability to support the creation of NLG benchmarks (Gardent et al., 2017).

rDF (rDF Working Group, 25 February 2014) statements are based on graph data models for representing knowledge.


Early work applying traditional semantic role labeling (SRL) techniques to iSRL was met with deflating results.

we observed that using both the head word and the closest coreferent non-pronoun head word is better than using the head word only on our development set.


This applies even more strongly to ON5V.

1 the words and phrases that fill each role are very diverse, as illustrated by the examples in Table 2.


We make the hypothesis that there are webpages in which it is probable to find definitions for highly specialized domains, because some feature a higher density of specialized vocabulary than others and some are explicitly characterized as explanatory.

the Digital Dictionary of the German Language (Digitales Wörterbuch der deutschen Sprache, DWDS 1 ) is a long term project of the Berlin-Brandenburg Academy of Sciences (BBAW).


To not come up with either an incorrect or too ambiguous annotation, we consider the use of this relation to help the system.

of this work, we make available the first public online repository containing manually annotated Spanish AMRs.


We also make the most of the richness of the encoding, while implementing some graphical choices to make their interpretation as clear as possible for end users.

it is assumed that from the point of view of users a graph is a more suitable tool to visualize and navigate through the terminological structure of a domain especially since some of its features can be exploited by designers to highlight different properties of relations and the nodes that these relations link.


In the next section, we refer to our published results for which we used the heads discovered by our head-finding algorithm; this extrinsic evaluation of our heuristics proved to be highly successful.

in the next section, we refer to our published results for which we used the heads discovered by our head-finding algorithm; this extrinsic evaluation of our heuristics proved to be highly successful.


Our automated phase generates theatre presentation as a candidate noun compound, with the label {CAUSE TO PERCEIVE, ENTITY}, implying that the presentation is by the theatre.


However, this seems like a corner case, as a presentation is more likely to be at the theatre.

one can also use hierarchy of frame elements (defined along with frame relations) to generalize the semantic relations.


Firstly, the approaches to creating some of the relation inventories and datasets are statistically motivated, rather than being linguistically motivated.

we do not include such examples in our dataset.


We propose a novel annotation approach to address this dilemma.

it's worth noting that more than twice as much statives in news have a stative parent (57%) than the ones having an eventive parent (24%), contributing to deep stative branches, while in narratives a much higher percentage of statives directly depend on an eventive (49%), contributing to a large number of short stative branches.


Other recent work in semantic parsing has also focused on programming languages, including regular expressions (Locascio et al., 2016), IFTTT scripts (Quirk et al., 2015), and SQL queries (Kwiatkowski et al., 2013;Iyer et al., 2017;Zhong et al., 2017).

other recent work in semantic parsing has also focused on programming languages, including regular expressions (Locascio et al., 2016), IFTTT scripts (Quirk et al., 2015), and SQL queries (Kwiatkowski et al., 2013;Iyer et al., 2017;Zhong et al., 2017).


Our experiments confirmed the hypothesis that some forms of world knowledge can improve existing AMR parsers.

our analyses of gold standard NER features also revealed some of the limitations of using world knowledge with existing AMR parsers.


The audio signal is first cut into segments by an audio segmentation algorithm.

furthermore, a search-engine enables the user to find all media files in which a keyword or phrase was spoken by searching the transcripts of spoken words provided by the automatic speech recognition.


Although retrieval as well as analysis is based on the transcription to date only half of the interviews are transcribed and saved as text files.

the interview is not structured by questions but is open for the course of memories coming into the interviewee's mind when telling his entire life story from birth and childhood into the present.


However, to achieve satisfactory results the speech recognition performance has to be improved further.

in this work we focus on acoustic modeling only and use our default language model described in subsection 2.2.4..


We also present the challenges caused by Oral History interviews for ASR-systems.

for evaluation we mostly used the clean subset of the DiSCo corpus in our previous work.


In the last step of analysis keywords are extracted from the ASR-generated transcript using a tf-idf (term frequencyinverse document frequency) approach.

we recently trained language models for specific tasks with more than one million words in the lexicon.


According to equation (2) we used a different room-impulse-response of the same room, than the one applied to the speech-signal, if one was available.

due to the computational time needed for training sophisticated neural networks we decided to use a 128 h subset of the GerTV1000h corpus for the experiments in this work.


Experiments with different content of the acoustic training data indicate that careful selection of phonotactical data could be advantageous when developing a speech corpus of limited size.

the Hjal project was successful as a pilot project, but has not been maintained.


The text was tokenized, thus separating punctuation from words, and lowercased.

due to the extra effort needed to collect such data and the possible loss in recording quality due to reading mistakes, a clear positive impact of special phonotactical data should be evident.


Details of the training set and processed test sets B1 and B2 of subtask 1 are shown in Table 6.

it is suggested that our augmentation strategy is effective and texts from forums are more suitable than news texts for the DSL task.


As we can see, with each feature removed, the performance of our system decreases to different extent except for the word unigrams.

identifying languages from very little data, from multilanguages input or discriminating between extremely similar languages are bottlenecks of this field (Ljubešic and Kranjcic, 2014;Zampieri et al., 2015b).


We approach this problem by successfully adapting a high-performance standard German speech   Table 6: WER [%] results of directly trained Swiss German speech recognitions systems using different types of pronunciation lexicons; standard German G2P (GG2P), combined data-driven Swiss German and standard German G2P (SGG2P) or grapheme sequences (Graph.)

in some use cases these resources are not available.


Over the last 15 years, MaryTTS (Schröder and Trouvain, 2001) has become one of the reference systems for open source text-to-speech synthesis (TTS).

this includes, at the very least, the sequence of phonemes, i.e., the pronunciation, but typically also other features related to 1 Examples of such dependencies in MaryttS include thirdparty libraries for text tokenization (Jtok), number expansion (ICU4J), and part-of-speech (POS) tagging (OpenNLP publishes voice-somename-xy the voice project resolves the processed data from the cloud (e.g., downloading from GitHub), runs all steps required to build a voice, and is published to Bintray.


ROUGE only analyzes the overlapping between the candidate compression and the references.

a Tesla Motors anunciou acordo para comprar a SolarCity por US$ 2,6 bilhões .


In Section 4 we analyze the results achieved by state-of-theart methods using our dataset.

they differ in many details of their grammar and lexicon.


On investigating, the feedback from the language expert was that the summaries are "good enough".

the monolingual extractive summaries were provided by Veooz 7 and Google translate was used for automatic translations.


The delay can be just a few hours, days or sometimes the news does not appear in the regional languages at all.

• The pluggable and generic architecture provides possibility of using the workbench for almost any language pair, and with any set of external tools to plug into the pipeline.


Moreover, there was almost no difference between a method using only the utterance position feature and others.

by dividing the utterances into topic units by topic segmentation, it is possible to take into account of topics of the discussion in conversation summarization.


Conversation summarization is useful to understand the content of conversations for both participants and nonparticipants.

other tags were appended to the end of each utterance for sentence-level identification.


The beginning part of the conversation usually contains a trigger of the discussion.


Gigaword corpora are also available in Arabic, Chinese, French, German, and Spanish.

over the past few years, artificial neural networks have shown promising results for abstractive summarization.


They use the Wikipedia featured articles as source for summary texts, since they are (i) well-written, (ii) comprehensive, (iii) well-researched, (iv) neutral, and (v) stable according to the Wikipedia featured article criteria 2 .

we were not able to retrieve results for a significant number of sentences with the search engine in particular for German sentences (English: 10,102 (14.20%), German: 9,325 (41.81%) sentences with an empty search results).


English and German lead sections have an average length of 13.87 and 8.99 sentences.

the evaluation results might be inaccurate if the summarization models are only evaluated on a few topic.


Finally, the model has low accuracy for the Non-Entailment cases.

the participant HOLE is omitted, but mentioned as mandatory in the pattern, i.e.


Target: influence (B2) Candidates: determine (B1), impress (A2), change A1In 2, where the target is the verb "influence" (B2), "determine" (B1) passes the grammatical reformation stage but fails at the definition stage because it does not cover the semantics of "influence" in the meaning of affecting the way someone thinks or behaves, though it might do so in another context where determination causes a change in someone's way of thinking.

this dataset has 137K pairs of simplified and unsimplified sentences that can be used for creating simplification rules.


In order to serve the research community, the adherence to the principles of Open Science not only demands clarity about the data services offered, but also about what is to be expected from the analysis tools offered.

language also plays a role as the reflection of scientific and societal knowledge, as an instrument for human communication, as one of the central components of the identity of individuals, groups, cultures or nations, as an instrument for human cognition and expression, as a formal system, and as historical records in need of documentation and preservation.


As in prior years, LDC has provided data and assessment for all six TAC KBP tracks including the endto-end Cold Start task, which builds a knowledge base from scratch along with evaluations with EDL, Slot Filling Events and Belief/Sentiment.

we computed fundamental frequency and log-scale pitch range controlling for individual and sex differences and correlating to neuropsychiatric tests and measures of gray matter atrophy.


• Implementation: There are two aspects of implementation of multi-language services: implementation of service composition, and embedding of composite services into application systems.

'); Figure 2: Example of invoking the atomic translation service KyotoUJServer ;; Create the atomic language service client ;; Only URL is different with that of previous example $client = ClientFactory::createTranslationClient ('http://langrid.org/service_manager/wsdl/ kyoto1.langrid:GoogleTranslate'); ;; Specify the service authentication information $client->setUserId('someUserId'); $client->setPassword('somePassword'); ;; Set invocation parameters and get the result $result = $client->translate( Language::get('en'), Language::get('ja'), 'Have a nice day!


This lack becomes most evident in Machine Translation applications for smaller European languages as well as other standard NLP tools and systems (according to approx.

the survey created a total of 634 responses and, considering the number of questions, a surprisingly high completion rate of 27%.


Further, participants were asked to define the research fields, areas and sub-areas, methods and applications they work on.

when asked about the biggest challenge the European Language Technology field is facing at the moment (Q14) around 16% of all provided survey answers stress that the neglect of smaller languages is a severe threat, which is leading to a fragmented rather than a united and multilingual Europe.


2003) topics to represent story structure and measured similarity between trajectories.

the disruption leads to high tension and the restoration of equilibrium lowers the tension.


The studies reviewed above exemplify a rich research tradition using statistical analysis of wine review corpora.

for the terminological features, we therefore considered the 15,357 most domain-specific terms, sorted by termhood.


Variability caused by the selection of the actual texts for the corpora might contribute to the observed differences.

the performance of both variants stabilizes for text lengths greater than 7,000 words.


For all methods, the central 50% of samples lie in a fairly narrow range of ±5 percent points around the median (the colored boxes).

we also observed considerable performance differences of roughly ten percent points between English, French and German.


Nevertheless, assuming the existence of an appropriate dataset, architectures for generating face descriptions are likely to share many of the challenges in the more familiar image description task.

by including country of origin, age and gender among the demographic details we request from participants, we hope to be able to address these questions in a more informed manner.


(Ordonez et al., 2011) and (Kuznetsova et al., 2012) are examples of retrieval in visual space; other approaches rely on retrieval in multimodal space (Hodosh et al., 2013, Socher et al., 2014.

this raises the possibility of more in-depth investigation of the use of language in a specific domain, including the identification and description of salient or distinguishing features (say, eye colour, the shape of a face, emotion or ethnicity) and the investigation of the conditions under which they tend to feature in human descriptions.


While the majority include physical features, there are also emotional descriptors, as well as analogical descriptions (as when a person is described as resembling a criminal; see Figure 1a) and inferred characteristics (such as the inferred nationality of the man in Figure 1a).

the system saved session variables, meaning that there was a limited time period during which participants could revisit the online interface and resume the description exercise from where they had left off.


The residuals of the model (i.e., respDev ∼ 1 + adj + σ p + µ p + σ p : µ p + (1|turker)) showed that the data was heteroskedastic.

in order to build technology that has the ability to answer questions relevant to national and global security, e.g., on food insecurity in certain parts of the world, one has to implement machine reading technology that extracts causal mechanisms from texts.


The resulting resource we provide 1 consists of a linear model for each adjective that takes as input the typical distribution of the item being modified and in turn provides the predicted size of the change.

responses generated using MTurk can be quite noisy, so to reduce the amount of noise in our data we excluded data based on several criteria.


Intuitively, by using word embeddings trained over a large corpus, we already know some of the underlying semantics of the unseen adjectives.

when describing events, authors often make use of gradable adjectives, i.e., adjectives (such as small) that can take a range of magnitudes or degrees (e.g., something can be a little small, very small, extraordinarily small, etc.)


To ensure that this difference between story-wise cross-validation and the default test set reflects the challenge of encountering new storyspecific content and isn't simply an artefact of the test set, we performed random cross-validation with held-out sets of the size of the average narrative.

by reducing the data requirements we make automatic transcription technology more feasible in a language documentation setting.


(2014) (pitch), and a combination of both (fbank+pitch).

we assess the performance of the systems as training data scales from 10 minutes to 224 minutes of spontaneous speech of a single Na speaker, and between 12 and 50 minutes for a single speaker of Chatino.


Since large amounts of text are used for language model training, such systems often do not incorporate pitch information for speech recog-nition of tonal languages (Metze et al., 2013), as they can instead rely on contextual information for tonal disambiguation via the language model (Le and Besacier, 2009;Feng et al., 2012) even though there is no computational burden in additionally using pitch features.


This paper presents a general use corpus for the Native American indigenous language Choctaw.

the data set comprises text, audio, and video.


This paper introduces a general use corpus for Choctaw, an American indigenous language.

of this history, the Choctaw language has few published works and little text representation online.


Currently only North Sami has a Wikipedia, but during the data collection sessions, interest was also sparkled within Inari Sami community to start a Wikipedia of their own.

the question also concerns how to define the appropriate ways to use the data, who can use it and who has the right to access the data.


Since the culture and traditions are carried by the language, there is always an inherent element of cultural knowledge in the topics and the use of the language, and although corpus collection can aim at a neutral goal of documenting a less-resourced language, in practice it may not be possible to collect a culturally "neutral" corpus.

(2018) use an end-to-end dialect recognition system based on the deep learning method and discuss its use as enabling technology for building interactive applications.


For example, the French word ordinateur (computer) is commonly used in Tunisian Arabic, so it might be transliterated as Awr-dynAtwr.

• Add new words that are not on the AUTO list.


For the individual task, the native speakers can do the task anywhere before the deadline as shown in Figure 11.

• T 2(L ID , L A ): From an estimated duration of 10 seconds per translation and a daily wage of JPY5,000/8 hours, the estimated total translation per day is 1 × 6 × 60 × 8 = 2, 880 and the estimated cost is JPY1.74 per  correct translation.


All body part nouns are traditionally considered to be relational (Laczkó et al., 2009).

this includes NomBank (Meyers et al., 2004), and other work on nominal semantic role labelling (Padó et al., 2008;Gerber and Chai, 2010).


To generate multilingual cognate tables, we employ an automatic method of clustering words from our lexical resources.

the character transition probabilities are obtained from alignment using GIZA++.


In this paper, we describe a new system to extract, index, search, and visualize entities in Wikipedia.

this system could be improved in many ways.


This system could be improved in many ways.

to most of these previous works, multilingual support is at the core of HED-WIG.


Then, we vote argument types of NL patterns with NL triples in the similar way as above.


Misclassifications were fixed and segments missing a role were assigned the appropriate one.

furthermore, by using these models, systems can increase their own interpretability, benefiting from the structured data for performing traceable reasoning and generating explanationsfeatures which are becoming even more valuable given the growing importance of Explainable AI (Gunning, 2017).


Following the same syntactic rules adopted for pre-annotation, missing supertypes were identified and the roles around it had its limits adjusted, while the remaining classification was kept unchanged.

in order to make the most of those resources, it is necessary to capture the semantic shape of natural language definitions and structure them in a way that favors both the information extraction process and the subsequent information retrieval, allowing the effective construction of semantic models from these data sources while keeping the resulting model easily searchable and interpretable.


Here, the information of a source sentence is complemented or eliminated in a translation.

this allows 90 different MT systems to be constructed.


(2011) constructed a parallel corpus of biomedical article titles from PUBMED in six languages.

images, tables, references), we would be infringing such copyright rules.


As we removed some parts from the articles (e.g.

europarl corpus is a transcription of speeches, thus inducing a greater linguistic variability.


If the negated event is only in the matrix clause, subordinates are usually excluded from the scope of negation.

he is CL not like show-off DE person 'He is a person who does not like to show off.'


Although keeping them annotated, we do not consider them any further.

the current annotations closely follow the same CONLL format of CONANDOYLE-NEG, marking each negation instance in a sentence as a set of three columns, for cue, event and scope respectively.


(4) can be in fact paraphrased as 'It is not the case that (Marx knew nothing about his customer).

since they are not annotated consistently in the English side, we decide *not* to mark these verbs as cues in Chinese.


Hindi-English Linked Wordnet contains bilingual dictionary entries created from the linked Hindi and English wordnets.

the HindiEn component of the corpus has also been used for the WMt 2014 shared task.


For English, we used true-cased representation for our experiments.

the translations included in the corpus were determined to be of good quality by annotators.


Therefore, the larger the parallel corpora are, the better the performance of the SMT system is.

to compute the similarity of two sentences, Lucene's original source code was modified so that the queries could be read from a text file and the most relevant sentences from another file could be given as the result of each query by the IR system.


The sentences extracted by both methods have been made available online.

the Penalty variable, which is the difference in the word number of the two sentences, makes the similarity score smaller when the difference is too large.


To build the translation systems, the default settings for Giza++ and SRILM toolkit were used.

there is a lack of such data available for everyone.


Before splitting the data into train, dev and test sets, we theorized that having too similar documents in the train and the test sets could lead to a skewed (too optimistic) evaluation of any supervised summarization methods.

the following documents were dropped: • with empty headline; • with abstract shorter than 10 words; • with full text shorter than 100 words; • with text-to-abstract ratio less than 4.


3 This indicates that Twain and James used 1 Distinctiveness Ratio: Measure of variability defined by the rate of occurrence of a word in a text divided by its rate of occurrence in another.

figure 1 shows two salient features for James and Twain: the adjectives broad and usual, where both decrease in usage over time.


After describing the resource, we show how the corpus can be used to detect changing features in literary style.

without also having examined combined models of or with other authors, it is not clear how close James and Twain are in terms of stylistic change regarding the features examined here.


The corpus which we consider in Section 4. comprises argumentation in the political domain.

four annotators were extensively trained in the use of Inference Anchoring Theory (IAT) (Budzynska and Reed, 2011) to analyse the television debates and Reddit discussions constituting the US2016 corpus that we take as a case in point in the current paper.


First, we generated questions for 10,000 nouns randomly sampled from the nouns that most frequently appear in the -de position in the TSUB-AKI corpus (Shinzato et al., 2008) of 600 million web pages.

we used 300-dimensional word embedding vectors pre-trained with wikipedia articles using Skip-gram with a negative-sampling algorithm (Mikolov et al., 2013).


In this paper we have demonstrated that it is feasible to automatically induce training data using parallel data without manual intervention.


The aim of our work is to project Named Entity (NE) annotations from several source languages into a target language for which there is not training data.

we project the automatic tagged Named Entities from three source languages to a fourth target language, performing all 4 permutations.


These two event mentions have conflicting place argument (Canada vs. Iowa), but they are interpreted as coreferential, because both mentions refer to the Life.Die event of "John Smith" (also mentioned as "the man").

eRe builds on the approach to labeling entities, relations, events and their attributes under a pre-defined taxonomy, following the approach used in Automatic Content extraction (ACe) (LDC, 2005;Walker et al., 2006;Song et al., 2015;Mott et al., 2016).


Annotators should not place the aggregate event in the same hopper as any of its subevents, and likewise should not place the subevents in a hopper with each other.

the corpus includes both positive examples of corpus-wide event hopper coreference, both crossdocument and cross-lingual, and also negative coreference judgements of many more potential event pairs.


The goal is that all encoders share the same sentence representation, i.e.

to previous works on cross-lingual document classification with RVC2, we explore training the classifier on all languages and transfer it to all others, ie.


We investigate citation networks in terms of content distance and study how the uncovered patterns can be used for identifying highly influential publications.

at the same time, the difference between the most similar and most dissimilar reference is higher for literature reviews (F15).


Although multiple applications can benefit from analyzing questions based on this criterion, the majority of datasets and taxonomies were designed for question answering systems.


ProcessProcedure -the question requests the process by which something happens (e.g., a natural/involuntary process of change) or the procedure for accomplishing a task.

with previous datasets with questions primarily used for question answering systems, we propose the first dataset that can facilitate the analysis of student responses in the educational environment.


The increased demand for structured knowledge has created considerable interest in relation extraction (RE) from large collections of documents.

the basic idea of our zero subject (entity) prediction is to perform tasks by finding the central entity being described within a paragraph without parsing.


We found that 29% of all questions are yes/no questions.

as an upper bound for model performance, we assess how well humans can solve our task.


But, humans will use external information from other modalities for disambiguation successfully if it becomes available.

the same holds true for the German examples of A2), in which RBG assigns alternative, improbable heads to the relative clauses.


Since we investigate the effect of external knowledge like visual scenes on language processing, information are not derived from the images automatically.

the relative clause is supposed to be attached to bed.


We describe and evaluate an element-wise, an item-based, and a hybrid approach, combining the two, to automatically calculate the CO2-footprints of recipes.

so far the footprint of a recipe was calculated with a manual process (O'Connor et al., 2018) which is timeconsuming and therefore too costly to be applied to a wide range of cooking recipes.


Our first approach, ingredient matching, calculates the CO2-footprint based on the ingredient descriptions that are matched to food products in a language resource and therefore suffers from the long tail problem.

we also store the amounts of each term in each recipe, so that we can quickly retrieve the amount of a term in a given recipe when comparing recipes.


the learning using the linear kernel took only 6.54 seconds.


In Table 3 we report the performances, for the Twitter data (filtered by our native volunteers to what they judged dialectal as the Gold Standard) and PCJD, in the form of confusion matrices.

we used the default dictionary that comes with the tool with nine features (Japan Information-Technology Promotion Agency, 1995).


This is likely due to the fact that our pivot model is based on newspaper data.

japanese poses an additional challenge since there is no word segmentation in its orthography.


To show how further refinement could help increase the accuracy of a classifier we trained a model using the same set of reduced features but this time only considering instances with more than or equal to 20 words.

each list contains function words/tags related to that category (Garcia-Barrero et al., 2013; Ryding, 2014).


The authors acknowledges the support of the DGE (Ministry of Economy) and DGA (Ministry of Defense): RAPID Project "DRIRS", referenced by number 172906108.

the verb law∼aj will have the same pattern.


Scikit learn (Pedregosa et al., 2011) is an open source python library that is a simple and efficient tool for data mining, data analysis and machine learning.

we carried out an experiment to determine the data size that will give us the highest performance.


MSA is the lingua franca amongst Arabic native speakers.

we choose Dialect Identification as the task to evaluate SDC and compare it with two other corpora.


"Listen, [female] dear, the problem lies both in you and them.


• Filtering out short tweets: To avoid ambiguity in very short tweets and hence difficulty and confusion in annotations, we also eliminated tweets that have less than three words.

to have a better-quality potential dataset for labeling, we cleaned the dataset as follows: • Filtering out non-Arabic tweets: Many Arab users post tweets written in multiple languages.


The results further suggest that common-off-the-shelf algorithms can reduce the amount of work required to retrieve relevant literature.


This is to be expected, since M represent those references the human annotators required the full text to judge, and it would be unreasonable to expect the ranker to be able to judge these based only on title and abstract.

unless otherwise stated, we use the default settings for all parameters.


The use of loss instead of other metrics such as accuracy or F-measure is supported by the fact that the loss itself models the actual behavior of the model.

nonetheless, the results of their experiments did not support any hypothesis of the method usefulness.


• Text length: distribution of the text lengths in the training dataset.

the test performance is expected to be slightly worse in comparison to the validation performance since the model was chosen to achieve the best validation performance regardless the test evaluation.


in this sense, terms like "distribution" or "measure" can be hypothesized to constitute motors of scientific innovation.


Such units are, indeed, characteristic of academic language and should not be ignored.

we attribute the permeation between these two classes to some of the features that give account for polysemy, as both Linguistics and Interdisciplinary contain more words with numerous possible senses, in addition to being the classes with highest word variety in terms of type-token ratio.


Importing, compressing and representing Wiki instances is an integral part of the system: A new Wiki is imported by providing a path to a compressed XML dump file (either current revisions only, or the full history version) to the XMLDumpParser.

we start by importing the history dump of the simple English wikipedia into a Neo4J database as backend using non-transactional mode 7 .


Generally speaking, researchers need efficient as well as manageable means to access wiki data.


In order to conduct the annotation, two undergraduate students were trained in the use of the MITI 4.0.

interestingly, the results show that the combination of n-grams and lexicons offer similar performance as the MiTi behaviors features.


People are interacting with cellphones, smart TVs, and computers on a daily basis using voice-based interfaces.

the problem of identifying interpersonal relationships is cast as a classification task.


One reason for the occurrence of the misunderstandings is that these systems rely on automated speech recognition (ASR) systems, which, despite showing strong improvements in performance, are far from perfect.

it should provide an intuition for further exploration.


Although these numbers may suggest that PALAVRAS achieved a better score than UDPipe, these numbers taken globally do not reveal the effective quantity of mistakes that were corrected.

the primary motivation to mine the DHBB came from the need to query the material looking for information that requires almost total reading of the whole body of texts.


In fact, even if "Machado Coelho" or "Castro Abreu" were in the lexicon and the phrase were "José Machado Coelho de Castro Abreu nasceu em 1931", only "José Machado de Castro" would be recognized, provided "José Machado Coelho de Castro Abreu" is not in the lexicon.

missingAppos an appositive relation was not detected, Figure 2d.


Another outcome was the difficulty to identify microblog languages in this corpus without using specialized lexical resources (Hamon et al., 2017).

specific and owner-based collections are developed to answer or evaluate a very specific task or are based upon data with specific ownership.